Attention mechanism plays an important role in computer vision [1]}. The aim of attention mechanism in CNN is to adaptively process features and focus on informative information. In recent years, some works have integrated attention modules into SR networks to improve their performance. Zhang et al. [2]} proposed a residual channel attention network (RCAN) to recover more high-frequency information for SISR. In their method, channel attention mechanism was applied to model the interdependencies across feature channels. Hu et al. [3]} proposed an SISR network including a channel attention module and a spatial attention module. These two attention modules are integrated by a gated fusion node to adaptively capture important information. Kim et al. [4]} integrated spatial and channel attention with a novel fused scheme to build both inter-and-intra channel relationship. Dai et al. [5]} designed a second-order channel attention (SOCA) mechanism to model the inherent feature correlations in intermediate layers. In the area of stereo image SR, parallax attention mechanism [6]}, [7]}, [8]}, [9]} was widely used to incorporate cross-view information from stereo image pairs.
